May 15, 2024, 4:42 a.m. | Georg Sch\"afer, Max Schirl, Jakob Rehrl, Stefan Huber, Simon Hirlaender

cs.LG updates on

arXiv:2405.08567v1 Announce Type: new
Abstract: This paper proposes a framework for training Reinforcement Learning agents using Python in conjunction with Simulink models. Leveraging Python's superior customization options and popular libraries like Stable Baselines3, we aim to bridge the gap between the established Simulink environment and the flexibility of Python for training bleeding edge agents. Our approach is demonstrated on the Quanser Aero 2, a versatile dual-rotor helicopter. We show that policies trained on Simulink models can be seamlessly transferred to …

abstract agents aim arxiv bleeding edge bridge cs.lg customization edge environment flexibility framework gap libraries paper popular python reinforcement reinforcement learning training type

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